7f1a6df202ff6a7fc2cb4b2b27e8b83a7ea7657f
7 Commits
| Author | SHA1 | Message | Date | |
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 kingchenc
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24e723fa7d |
feat: Family 02 Momentum Oscillators — RVI / PGO / KST / SMI / Laguerre / Connors / Inertia (#40)
* feat(rvi): add Relative Vigor Index
Dorsey's RVI = SMA(close - open, period) / SMA(high - low, period) over
a rolling window of period candles. Candle input, single parameter
period (default 10). Positive on average-bullish windows, negative on
average-bearish. Holds the previous value if the entire window has
zero range (denominator undefined).
Reference: Donald Dorsey, also pandas-ta rvi.
Touchpoints: rvi.rs + mod.rs + lib.rs re-export, PyRvi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values reference,
RviNode (4-column OHLC batch) + index.d.ts/index.js + indicators.test
.js factory + reference, WasmRvi + make_candle_ohlc helper, candle-fuzz
target + criterion bench, README + CHANGELOG.
* feat(pgo): add Pretty Good Oscillator
Mark Johnson's PGO = (close - SMA(close, period)) / EMA(TR, period).
Counts roughly how many ATR-equivalents the close sits from its
period-bar mean. Candle input, single parameter period (default 14).
Johnson's heuristic uses +3/-3 crossings as entry signals.
Touchpoints: pgo.rs + mod.rs + lib.rs re-export, PyPgo + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-close
reference, PgoNode (h/l/c) + index.d.ts/index.js + indicators.test.js
factory + reference, WasmPgo, candle-fuzz target + bench, README +
CHANGELOG.
* feat(kst): add Know Sure Thing (Pring)
Pring's long-horizon momentum oscillator: weighted sum of four
SMA-smoothed ROC series with fixed weights 1, 2, 3, 4, plus an SMA
signal line. Nine parameters (four ROC periods, four SMA periods, one
signal period); classic() applies Pring's recommended defaults.
Multi-output indicator emitting KstOutput { kst, signal }.
Touchpoints: kst.rs + mod.rs + lib.rs re-export, PyKst + __init__.py
+ test_new_indicators MULTI + test_known_values flat-input reference,
KstNode + KstValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmKst (manual JsValue object), scalar-fuzz
target (handled outside the f64-output drive helper), README +
CHANGELOG.
* feat(smi): add Stochastic Momentum Index (Blau)
Blau's doubly-EMA-smoothed bounded oscillator: measures the close's
displacement from the centre of the recent high-low range, scaled by
the smoothed range. Candle input, three parameters (period, d_period,
d2_period) with defaults 5 / 3 / 3.
Internally feeds both the displacement-EMA stack and the range-EMA
stack on every candle so they warm up in parallel (gating either
behind the other starves the second by one input).
Touchpoints: smi.rs + mod.rs + lib.rs re-export, PySmi + __init__.py
+ test_new_indicators CANDLE_SCALAR + test_known_values flat-input
reference, SmiNode + index.d.ts/index.js + indicators.test.js factory
+ reference, WasmSmi, candle-fuzz target, README + CHANGELOG.
* feat(laguerre-rsi): add Ehlers Laguerre RSI
Four-stage Laguerre polynomial filter wrapped in an RSI-style up/down
accumulator. Single gamma in [0, 1] (default 0.5) trades lag for
smoothness. State is seeded by setting all four L_i to the first input
so a constant series stays at the neutral 50. Output clamped to
[0, 100] to absorb floating-point rounding.
Reference: Ehlers, Time Warp - Without Space Travel, 2002.
Touchpoints: laguerre_rsi.rs + mod.rs + lib.rs re-export, PyLaguerreRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values neutral
reference, LaguerreRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmLaguerreRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(connors-rsi): add Connors RSI (CRSI)
Larry Connors' 3-component aggregate: RSI(close), RSI(streak), and
PercentRank of the 1-period return over the last period_rank returns.
Each component is bounded in [0, 100] so the aggregate is too.
Three parameters (period_rsi, period_streak, period_rank) with
defaults 3 / 2 / 100. Streak tracks consecutive up/down runs (resets
to 0 on unchanged close).
Touchpoints: connors_rsi.rs + mod.rs + lib.rs re-export, PyConnorsRsi
+ __init__.py + test_new_indicators SCALAR + test_known_values bounded
reference, ConnorsRsiNode + index.d.ts/index.js + indicators.test.js
factory + reference, WasmConnorsRsi via scalar macro, scalar-fuzz
target, README + CHANGELOG.
* feat(inertia): add Dorsey Inertia (RVI + LinReg)
Donald Dorsey's Inertia — a LinearRegression smoothing of the RVI
series. Endpoint of an n-bar least-squares fit of RVI is the indicator
reading. Preserves trend direction while damping the ratio. Candle
input, two parameters (rvi_period, linreg_period) with defaults 14 / 20.
Touchpoints: inertia.rs + mod.rs + lib.rs re-export, PyInertia +
__init__.py + test_new_indicators CANDLE_SCALAR + test_known_values
constant reference, InertiaNode (4-column OHLC batch) + index.d.ts /
index.js + indicators.test.js factory + reference, WasmInertia,
candle-fuzz target, README + CHANGELOG.
* test(kst): Move KST out of MULTI dict (it is scalar-input)
KST sits in the MULTI dict (candle-input, multi-output) but its
update() takes a single f64, not a candle tuple. The shared streaming
loop in test_multi_streaming_matches_batch fed the OHLCV tuple in,
which crashed with `TypeError: argument 'value': must be real number,
not tuple` on every Python matrix entry.
Split into a new MULTI_SCALAR_INPUT dict with its own test function
that feeds the close-price stream as floats. KST is currently the
only such indicator; structure is ready for future scalar-input
multi-output additions (e.g. some MACD-shaped indicators).
* test(coverage): Cover SMI zero-range and ConnorsRsi zero-prev cold paths
codecov/patch on PR 40 flagged two uncovered defensive branches:
- SMI returns self.current early when the smoothed range collapses to
zero (`r2 <= 0.0`) so the formula stays defined. Exercised by feeding
bars where high == low.
- ConnorsRsi skips the ROC ring-buffer update when the previous price
is exactly zero so the divide-by-zero in `(input - prev) / prev` is
impossible. Exercised by seeding the first bar at 0.0.
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kingchenc
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466faddd87 |
feat: Family 01 Moving Averages — ALMA / McGinley / FRAMA / VIDYA / JMA / Alligator / EVWMA (#39)
* feat(alma): add Arnaud Legoux Moving Average
Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.
Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.
Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(mcginley): add McGinley Dynamic moving average
John McGinley's self-adjusting moving average with the recurrence
MD + (price - MD) / (0.6 * period * (price / MD)^4). Speeds up when
price falls below the indicator and damps when price runs above the
indicator. Seeded with the simple average of the first period inputs.
Reference: McGinley, Technical Analysis of Stocks & Commodities, 1990.
Touchpoints:
- crates/wickra-core: mcginley_dynamic.rs + mod.rs + lib.rs re-export
- bindings/python: PyMcGinleyDynamic + __init__.py + test_new_indicators
+ test_known_values reference
- bindings/node: McGinleyDynamicNode (scalar macro) + index.d.ts/index.js
+ indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers McGinleyDynamic(10)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(frama): add Fractal Adaptive Moving Average
Ehlers' FRAMA adapts its smoothing constant to the fractal dimension of
the recent window: tight tracking in trends, heavy smoothing in chop.
Uses the close-only variant where max/min over each window half drive
the dimension estimate. Period must be even (default 16).
Reference: Ehlers, Fractal Adaptive Moving Average, 2005.
Touchpoints:
- crates/wickra-core: frama.rs + mod.rs + lib.rs re-export
- bindings/python: PyFrama + __init__.py + test_new_indicators +
test_known_values reference (constant series + uptrend tracking)
- bindings/node: FramaNode (scalar macro) + index.d.ts/index.js +
indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers Frama(16)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry
* feat(vidya): add Variable Index Dynamic Average
Chande's VIDYA — an EMA whose alpha scales with |CMO(cmo_period)| / 100.
Strong directional momentum lifts the smoothing constant toward the
EMA-of-period rate; flat or choppy windows shrink it toward zero so
VIDYA coasts on its previous value. Two parameters: period (14) and
cmo_period (9). Reuses the existing wickra-core Cmo internally.
Reference: Chande, Stocks & Commodities, 1992.
Also fixes a silent gap from
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kingchenc
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efcd6216c1 |
feat(bindings): expose RollingVWAP in Python, Node and WASM (R4)
The rolling-window VWAP indicator (`wickra_core::RollingVwap`) was only available in the Rust crate, even though the README's Volume-family table already advertised "VWAP (cumulative + rolling)" as a cross- language feature. Users on Python, Node or in the browser had to fall back to the cumulative `VWAP` or re-implement the rolling variant themselves. This commit closes the gap end-to-end: - Python: `wickra.RollingVWAP(period)` — same constructor / `update` / `batch` / `reset` / `is_ready` / `warmup_period` surface as `VWAP`, plus a `period` property and a typed `__repr__`. The `__init__.py` re-exports it and `__all__` lists it; the `.pyi` stub matches. - Node: `RollingVWAP(period)` — napi class with the same lifecycle, exported from `index.js` and declared in `index.d.ts`. - WASM: `RollingVWAP(period)` — wasm-bindgen class with the same `Float64Array` I/O as `VWAP`. Tests added: - Python: `test_rolling_vwap_streaming_matches_batch` — exercises `update == batch` plus the full lifecycle on the shared OHLC fixture. - Node: `RollingVWAP` row in the `candleScalar` parity table — covered by the generic streaming-vs-batch + lifecycle harness. - WASM: dedicated `wasm-bindgen-test` mirrors the Python test. The wiki page `Indicator-Vwap.md` drops the "Rust-only" caveat and gains Python / Node / WASM examples. |
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kingchenc
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d2f99efd78 |
F13c: restructure the indicator catalogue into eight families
The original taxonomy was four classical families plus a statistics group, with the F1-F12 expansion slotted in as sub-categories. This regroups the whole 71-indicator catalogue into eight top-level families, each with at least five members: Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9), Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5), Volume (9), Price Statistics (7). - Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71 indicator pages moved with `git mv`. Every internal cross-link is normalised to `../<family>/Indicator-X.md`, each page's `Family` field is set to its new family, and two pre-existing `../Indicator-Chaining.md` links (should have been `../../`) are corrected. A link check confirms every relative wiki link resolves. - Indicators-Overview.md fully rewritten around the eight families; Home.md indicator reference and the README family table follow suit. - Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the 46-indicator expansion (25 -> 71) and the eight-family taxonomy. - Tests: Node indicators.test.js and Python test_new_indicators.py cover all eight new indicators (Node 91/91, Python 117/117 green). cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests, 25 data tests and 74 doctests green. |
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kingchenc
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2f3b5cc3be |
F-Abschluss: wire the Python package, refresh docs and extend the test suites
Finalises the F1-F12 indicator expansion (25 -> 63 indicators). - Python `wickra/__init__.py`: import and re-export all 63 indicators, grouped by family, with a matching `__all__`. The package previously exposed only the original 25 even though the compiled module and the `.pyi` stubs already carried the rest. - Docs: `Home.md` and `README.md` indicator counts and family tables updated to 63; `Indicators-Overview.md` already restructured per family in F10-F12; `Warmup-Periods.md` gains all 38 new indicators across the single- and multi-output tables (and the stale two-arg `Psar::new` example is corrected to three args); `CHANGELOG.md` `[Unreleased]` lists every new indicator by family. - Tests: `bindings/node/__tests__/indicators.test.js` covers all 63 indicators (streaming==batch plus four new reference-value checks), 80/80 green; new `bindings/python/tests/test_new_indicators.py` covers the 38 additions (streaming==batch, shapes, reference values, lifecycle), Python suite 105/105 green. - `bindings/node/index.js` regenerated by `napi build`. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests, 25 data tests, 66 doctests, 80 Node tests and 105 Python tests green; `cargo check -p wickra-wasm --tests` green. |
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kingchenc
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41d52ec5be |
B9: raise ValueError instead of panicking on non-contiguous arrays
Every Python batch() did prices.as_slice().expect("contiguous"), so a
non-contiguous NumPy input (e.g. a strided view) aborted with a Rust
panic instead of a catchable exception. as_slice() failures now map to a
PyValueError pointing at np.ascontiguousarray; the scalar / MACD /
Bollinger batch methods that returned a bare array were lifted to
PyResult so the error can propagate. Adds input-validation tests
(non-contiguous arrays, unequal-length candle batches, ROC/TRIX
defaults). All 60 Python tests pass against the freshly built wheel.
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kingchenc
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3be267cb03 |
Wickra 0.1.0: streaming-first technical indicators
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
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